Khan Mohd Imran, Taehwan Park, Cho Yunseong, Scotti Marcus, Priscila Barros de Menezes Renata, Husain Fohad Mabood, Alomar Suliman Yousef, Baig Mohammad Hassan, Dong Jae-June
Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
Postgraduate Program in Bioactive Natural and Synthetic Products, Federal University of Paraíba, João Pessoa, Brazil.
Front Neurosci. 2023 Mar 3;16:1007389. doi: 10.3389/fnins.2022.1007389. eCollection 2022.
Alzheimer's disease (AD) is the most studied progressive eurodegenerative disorder, affecting 40-50 million of the global population. This progressive neurodegenerative disease is marked by gradual and irreversible declines in cognitive functions. The unavailability of therapeutic drug candidates restricting/reversing the progression of this dementia has severed the existing challenge. The development of acetylcholinesterase (AChE) inhibitors retains a great research focus for the discovery of an anti-Alzheimer drug.
This study focused on finding AChE inhibitors by applying the machine learning (ML) predictive modeling approach, which is an integral part of the current drug discovery process. In this study, we have extensively utilized ML and other approaches to search for an effective lead molecule against AChE.
The output of this study helped us to identify some promising AChE inhibitors. The selected compounds performed well at different levels of analysis and may provide a possible pathway for the future design of potent AChE inhibitors.
阿尔茨海默病（AD）是研究最多的进行性神经退行性疾病，影响全球4000万至5000万人。这种进行性神经退行性疾病的特点是认知功能逐渐且不可逆转地下降。缺乏限制/逆转这种痴呆症进展的治疗候选药物加剧了现有挑战。乙酰胆碱酯酶（AChE）抑制剂的开发仍然是抗阿尔茨海默病药物发现的一个重要研究重点。
本研究专注于通过应用机器学习（ML）预测建模方法来寻找AChE抑制剂，这是当前药物发现过程的一个组成部分。在本研究中，我们广泛利用ML和其他方法来寻找针对AChE的有效先导分子。
本研究的结果帮助我们鉴定出一些有前景的AChE抑制剂。所选化合物在不同分析水平上表现良好，可能为未来设计强效AChE抑制剂提供一条可能的途径。
